{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2H6YLCPOXvZ",
        "outputId": "463a22f7-885f-4624-f487-e54156c4cee7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (11.4.5.107)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
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            "Requirement already satisfied: fsspec[http]>=2021.05.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2023.6.0)\n",
            "Requirement already satisfied: huggingface-hub>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.23.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from evaluate) (24.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets>=2.0.0->evaluate) (3.14.0)\n",
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            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.3)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->evaluate) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install numpy\n",
        "!pip install pandas\n",
        "!pip install accelerate\n",
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H-LANn-hUlZh"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "np.random.seed(11)\n",
        "import torch\n",
        "torch.manual_seed(11)\n",
        "import random\n",
        "random.seed(11)\n",
        "\n",
        "import transformers\n",
        "import pandas as pd\n",
        "\n",
        "from datasets import load_metric\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import Trainer\n",
        "from transformers import TrainingArguments\n",
        "from transformers import BertForSequenceClassification\n",
        "from transformers import BertTokenizerFast\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM--HAJWVJ0D",
        "outputId": "5dec7a51-8db1-441e-c23f-563b71f98b39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1adWrXv4W902QRU74oMT6xGFU16S3lbLW\n",
            "To: /content/train_dev.csv\n",
            "100% 615k/615k [00:00<00:00, 140MB/s]\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=152S49WDTr0uSUeg4yYwCeByi6a6rHro4\n",
            "To: /content/test.csv\n",
            "100% 122k/122k [00:00<00:00, 124MB/s]\n"
          ]
        }
      ],
      "source": [
        "# !gdown 16FSh45xge5RybunR6-4tER8Vi_pXXx4a # download the entire dataset\n",
        "!gdown 1adWrXv4W902QRU74oMT6xGFU16S3lbLW # train_dev\n",
        "!gdown 152S49WDTr0uSUeg4yYwCeByi6a6rHro4 # test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304,
          "referenced_widgets": [
            "29acbf1fd1f44265915dcedb835e71cd",
            "d029ea6541ee481686f4c8688ae062f4",
            "d145e93c0359460eb516575c530fa04a",
            "7a4990aba2704949a2c68dd860e9afe3",
            "4f69213a3c3f4266842a3a4e24bcb91d",
            "cce6907d949f4276a48ce34c50b6295c",
            "35648abfde8f4bbaa3ac2bb19a753455",
            "d62ca72ffd304c31a486a30d10074b23",
            "a617efc505bc47289d610dc100d95260",
            "183ea84d6fcf41c6a3225eb5176f2a00",
            "efdccb95e9ad4e908ed8dbe1a56055f5",
            "ce2f9bf5e06e444e8254bb9b14f43d8d",
            "d6a0aac822ef4c408ce7c252a544cd85",
            "0f4cb246c40a4892b6bc5c24d29a0259",
            "1de015ca47d94d3a9a0db8d24b764253",
            "b1eae8078fae4788a84f5ad34c6a0e51",
            "be5dd622df1644c5861680398b563c1c",
            "9cf81250916949f39091ad7ef78a5be1",
            "0b1da106c02148e480c8f5e2d925cbf3",
            "2029a135a27746faa1b0c9c7a9398b23",
            "3184d80f1782423395c6bd172a3135cf",
            "10a863a6011d459e9917f24c4d7c984a",
            "01915ec698944192a98ddc755d18fb83",
            "c682928781034026bd6cbb94e4dea4bb",
            "c113ccdeb91c4bdbb56d17b9e148e9e9",
            "05e18665e7304ff69b255e51c1420418",
            "536bfab5642740379032a3ac80b52f15",
            "83b252aae3114b03ac9fc9525a5b35dd",
            "830fd54af89440b4a14671e62be10f02",
            "904a8e27eb2f472da9442f59c69657d4",
            "e7a817843c4e4b9f9d79fc52cb3d1f22",
            "780831decfbc4b4dbe6cb69bbc0f1e69",
            "37a8f59fae2b4ad9aaa618c64f2bc260",
            "b9bd38395c954842b5b918021b63ffec",
            "801d0a42c20640c79028fc75462e91c3",
            "d6603a58f8b64cd7b5b033a8f75b29b0",
            "0d2ed921c6a8405f9005ec2cc14d6d2d",
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            "a2cfcd85cacf47a9887c4b5f64a29cc0",
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            "51013d55d5334ec99b5a1dac93c980e8",
            "b5943268c5f94b2e8c2044214f9f5039",
            "281d020504754c2e80b09322c846facd"
          ]
        },
        "id": "hTL4dINeVkI3",
        "outputId": "b0c4345e-5737-4ec8-b924-fd28df3cfb4d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "29acbf1fd1f44265915dcedb835e71cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce2f9bf5e06e444e8254bb9b14f43d8d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "01915ec698944192a98ddc755d18fb83"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9bd38395c954842b5b918021b63ffec"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')\n",
        "mlength = 512\n",
        "\n",
        "train = pd.read_csv(\"/content/train_dev.csv\")\n",
        "test = pd.read_csv(\"/content/test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s9TlhqaVZmgP"
      },
      "outputs": [],
      "source": [
        "class PsyDataset(torch.utils.data.Dataset):\n",
        "    def __init__(self, encodings, labels):\n",
        "        self.encodings = encodings\n",
        "        self.labels = labels\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n",
        "        item['labels'] = torch.tensor(self.labels[idx])\n",
        "        return item\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JmbJrfELfs9G"
      },
      "outputs": [],
      "source": [
        "def train_dev_split(df, seed):\n",
        "  X_train, X_dev, Y_train, Y_dev, indices_train, indices_test = train_test_split(df['text'], df[\"label\"], df.index, test_size=len(df)//10, random_state = seed)\n",
        "  return [X_train, X_dev, Y_train, Y_dev] # effectively save 10% as validation\n",
        "\n",
        "def data_prep(df, seed = 11):\n",
        "  X_train, X_dev, Y_train, Y_dev = train_dev_split(df, seed)\n",
        "\n",
        "  X_train = list(X_train)\n",
        "  X_dev = list(X_dev)\n",
        "  X_test = list(test[\"text\"])\n",
        "\n",
        "  Y_train = list(Y_train)\n",
        "  Y_dev = list(Y_dev)\n",
        "  Y_test = list(test[\"label\"])\n",
        "\n",
        "  train_encodings = tokenizer(X_train, truncation=True, padding=True, max_length=mlength)\n",
        "  dev_encodings = tokenizer(X_dev, truncation=True, padding=True, max_length = mlength)\n",
        "  test_encodings = tokenizer(X_test, truncation=True, padding=True, max_length= mlength)\n",
        "\n",
        "  train_dataset = PsyDataset(train_encodings, Y_train)\n",
        "  dev_dataset = PsyDataset(dev_encodings, Y_dev)\n",
        "  test_dataset = PsyDataset(test_encodings, Y_test)\n",
        "  return train_dataset, dev_dataset, test_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nRKm_KMi4d5Y"
      },
      "outputs": [],
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    metric1 = load_metric(\"precision\", trust_remote_code=True)\n",
        "    metric2 = load_metric(\"recall\", trust_remote_code=True)\n",
        "    metric3 = load_metric(\"f1\", trust_remote_code=True)\n",
        "\n",
        "    logits, labels = eval_pred\n",
        "    predictions = np.argmax(logits, axis=-1)\n",
        "\n",
        "    precision = metric1.compute(predictions=predictions, references=labels)[\"precision\"]\n",
        "    recall = metric2.compute(predictions=predictions, references=labels)[\"recall\"]\n",
        "    f1 = metric3.compute(predictions=predictions, references=labels)[\"f1\"]\n",
        "\n",
        "    return {\"precision\": precision, \"recall\": recall, \"f1\": f1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KyOxYddvgFkP"
      },
      "outputs": [],
      "source": [
        "# NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./PM-results',       # output directory\n",
        "    num_train_epochs=10,             # total number of training epochs\n",
        "    per_device_train_batch_size=64,  # batch size per device during training\n",
        "    per_device_eval_batch_size=128,  # batch size for evaluation\n",
        "    warmup_steps=0,                  # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.01,               # strength of weight decay\n",
        "    logging_dir='./logs',            # directory for storing logs\n",
        "    logging_steps=100,\n",
        "    learning_rate = 1e-5,\n",
        "    save_strategy= \"epoch\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    load_best_model_at_end= True,\n",
        "    metric_for_best_model=\"f1\",\n",
        "    seed = 11,\n",
        ")\n",
        "\n",
        "def model_init():\n",
        "    return BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "_fVi9IbRDSgB",
        "outputId": "23007e8b-ab00-46f6-cf9f-e650f17c7110"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a64ae8446aff47febda6cdb4c5a2c653"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 01:40, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.651416</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.695652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.581810</td>\n",
              "      <td>0.705882</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.827586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.493914</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.409874</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.846154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.374900</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.846154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.300795</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.259910</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.262554</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.916667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.226225</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.194946</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.192489</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.191605</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.199463</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.200703</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.198046</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.199067</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.201635</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.204190</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.206142</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.206971</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.956522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-8aed5d6a12e8>:2: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric1 = load_metric(\"precision\", trust_remote_code=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "name": "stdout",
          "text": [
            "Size: 200, Accuracy: 0.778, Precision: 0.7354014598540146, Recall: 0.8395833333333333, F1: 0.7840466926070039, Time: 109.46430087089539\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='160' max='160' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [160/160 01:55, Epoch 20/20]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.546111</td>\n",
              "      <td>0.735294</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>0.806452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.372166</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.901961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.363011</td>\n",
              "      <td>0.956522</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>0.862745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.326231</td>\n",
              "      <td>0.794118</td>\n",
              "      <td>0.964286</td>\n",
              "      <td>0.870968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.304481</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.303463</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.905660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.324961</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.872727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.380285</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.399335</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.298904</td>\n",
              "      <td>0.896552</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.912281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.301983</td>\n",
              "      <td>0.961538</td>\n",
              "      <td>0.892857</td>\n",
              "      <td>0.925926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.502375</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.554311</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.439339</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.905660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.399295</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.905660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.488644</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.503512</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.503510</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.500557</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.140600</td>\n",
              "      <td>0.500807</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.821429</td>\n",
              "      <td>0.884615</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size: 500, Accuracy: 0.843, Precision: 0.8210735586481114, Recall: 0.8604166666666667, F1: 0.8402848423194303, Time: 120.46109771728516\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='150' max='150' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [150/150 01:09, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.634203</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.716981</td>\n",
              "      <td>0.690909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.544200</td>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.830189</td>\n",
              "      <td>0.778761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.455856</td>\n",
              "      <td>0.836364</td>\n",
              "      <td>0.867925</td>\n",
              "      <td>0.851852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.408848</td>\n",
              "      <td>0.839286</td>\n",
              "      <td>0.886792</td>\n",
              "      <td>0.862385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.396089</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.905660</td>\n",
              "      <td>0.872727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.391980</td>\n",
              "      <td>0.839286</td>\n",
              "      <td>0.886792</td>\n",
              "      <td>0.862385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.419500</td>\n",
              "      <td>0.389107</td>\n",
              "      <td>0.839286</td>\n",
              "      <td>0.886792</td>\n",
              "      <td>0.862385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.419500</td>\n",
              "      <td>0.393145</td>\n",
              "      <td>0.827586</td>\n",
              "      <td>0.905660</td>\n",
              "      <td>0.864865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.419500</td>\n",
              "      <td>0.385938</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.905660</td>\n",
              "      <td>0.880734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.419500</td>\n",
              "      <td>0.384566</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.905660</td>\n",
              "      <td>0.880734</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size: 1000, Accuracy: 0.846, Precision: 0.8299595141700404, Recall: 0.8541666666666666, F1: 0.8418891170431211, Time: 75.44751119613647\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='710' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [710/710 03:00, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.402929</td>\n",
              "      <td>0.770909</td>\n",
              "      <td>0.883333</td>\n",
              "      <td>0.823301</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.484600</td>\n",
              "      <td>0.356105</td>\n",
              "      <td>0.791822</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.836935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.296100</td>\n",
              "      <td>0.388666</td>\n",
              "      <td>0.760417</td>\n",
              "      <td>0.912500</td>\n",
              "      <td>0.829545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.296100</td>\n",
              "      <td>0.364061</td>\n",
              "      <td>0.834677</td>\n",
              "      <td>0.862500</td>\n",
              "      <td>0.848361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.209000</td>\n",
              "      <td>0.377707</td>\n",
              "      <td>0.830645</td>\n",
              "      <td>0.858333</td>\n",
              "      <td>0.844262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.149800</td>\n",
              "      <td>0.399399</td>\n",
              "      <td>0.847107</td>\n",
              "      <td>0.854167</td>\n",
              "      <td>0.850622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.149800</td>\n",
              "      <td>0.479230</td>\n",
              "      <td>0.783394</td>\n",
              "      <td>0.904167</td>\n",
              "      <td>0.839458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.116200</td>\n",
              "      <td>0.454088</td>\n",
              "      <td>0.828794</td>\n",
              "      <td>0.887500</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.091500</td>\n",
              "      <td>0.464496</td>\n",
              "      <td>0.830709</td>\n",
              "      <td>0.879167</td>\n",
              "      <td>0.854251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.072700</td>\n",
              "      <td>0.473919</td>\n",
              "      <td>0.815385</td>\n",
              "      <td>0.883333</td>\n",
              "      <td>0.848000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size: 5000, Accuracy: 0.875, Precision: 0.865979381443299, Recall: 0.875, F1: 0.8704663212435233, Time: 185.88179683685303\n"
          ]
        }
      ],
      "source": [
        "from collections import defaultdict\n",
        "from evaluate import load\n",
        "import time\n",
        "\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
        "\n",
        "results = defaultdict(list)\n",
        "sizes = [200, 500, 1000, 5000]\n",
        "\n",
        "for size in sizes:\n",
        "  start = time.time()\n",
        "  if size <= 500:\n",
        "    training_args.num_train_epochs = 20\n",
        "    training_args.learning_rate = 3e-5\n",
        "  else:\n",
        "    training_args.num_train_epochs = 10\n",
        "    training_args.learning_rate = 1e-5\n",
        "  train_dataset, dev_dataset, test_dataset = data_prep(train[:size])\n",
        "  trainer = Trainer(\n",
        "      model_init=model_init,               # the instantiated 🤗 Transformers model to be trained\n",
        "      args=training_args,                  # training arguments, defined above\n",
        "      train_dataset=train_dataset,         # training dataset\n",
        "      eval_dataset=dev_dataset,            # evaluation dataset\n",
        "      compute_metrics=compute_metrics,     # compute_metrics\n",
        "      )\n",
        "  trainer.train()\n",
        "  output = trainer.predict(test_dataset)\n",
        "  accuracy = accuracy_score(output.label_ids, np.argmax(output.predictions, axis=-1))\n",
        "  precision = precision_score(output.label_ids, np.argmax(output.predictions, axis=-1))\n",
        "  recall = recall_score(output.label_ids, np.argmax(output.predictions, axis=-1))\n",
        "  f1 = f1_score(output.label_ids, np.argmax(output.predictions, axis=-1))\n",
        "  t = time.time() - start\n",
        "  results[size].append(accuracy)\n",
        "  results[size].append(precision)\n",
        "  results[size].append(recall)\n",
        "  results[size].append(f1)\n",
        "  results[size].append(t)\n",
        "  print(f\"Size: {size}, Accuracy: {accuracy}, Precision: {precision}, Recall: {recall}, F1: {f1}, Time: {t}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, val in results.items():\n",
        "  print(key, val)"
      ],
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